Determinants of Energy Consumption in the Dairy Industry: A Case Study in Poland

Wojdalski, Janusz; Ligenza, Przemysław; Postuła, Marta; Dróżdż, Bogdan; Niżnikowski, Roman

doi:10.2478/oszn-2023-0017

Full Article

1.

INTRODUCTION

In 2021, Polish industrial plants consumed 1,079,886 TJ of electricity and 253,285 TJ of heat. Twenty food processing sectors were responsible for 8% of the total energy consumption and 6% of the total heat consumption in the food industry [SWAID 2023].

Milk and dairy production are among the largest sectors of this industry. In 2018, dairy plants purchased 9,043 million litres of milk, which accounts for 75.0% of the total output of Polish milk farms. Dairy plants also have a high demand for energy and water. The energy consumption of Poland’s food-processing sector has increased by around 10% in recent years, but there are no reliable data for assessing the dairy sector’s contribution to that increase. It has been established, however, that the Polish dairy sector is the second largest energy consumer on the agri-food market. The demand for heat in Polish dairy plants accounts for approximately 87 – 90% of total energy demand.

Dairy plants differ in output and production technology, and this affects their energy demands. Food-processing plants generally have been striving to implement more stringent food hygiene standards, reduce water consumption, and increase the effectiveness of their wastewater management systems [Chang et al. 2016]. The microbiological qualities of milk determine its thermal processing requirements, which may include the demand for cooling water in heat exchangers and the demand for cleaning water. Research on energy consumption in the food processing sector can provide valuable inputs for the transition to renewable energy [Mitroczuk 2022].

2.

OBJECTIVE AND SCOPE OF THE STUDY

–
To conduct a detailed analysis of energy consumption in four types of dairy plants with different production profiles, and to identify the most energy-intensive operations in these plants;
–
To generate new information for modelling energy consumption in dairy plants based on a review of the literature and descriptive statistics;
–
To describe correlations between engineering and production factors, including production profile, output, equipment, and energy consumption, in four types of dairy plants (taking into account the automation of production processes); and
–
To formulate recommendations for industrial-practice and environmental-performance guidelines that could reduce energy consumption in the dairy sector, increase energy efficiency in dairy production, and promote the implementation of cleaner production standards.

These research aims are based on the objectives formulated by Bunse et al. [2011], who pointed out the gap between the research needs reported by manufacturing plants and the existing published scientific literature. The research problem formulated for this study was validated empirically based on the results of both qualitative and quantitative research. Qualitative research involved descriptive analysis, while quantitative research involved the systematization of statistical data from the analysed sources.

3.

LITERATURE REVIEW

3.1.

Energy Consumption and Environmental Performance of Dairy Plants

Based on descriptive statistics, the literature on energy consumption and the environmental performance of the dairy sector is presented in Table 1. The relevant information can be used to generate databases, optimize production processes, reduce energy consumption and pollutant emissions in the dairy sector. In Table 1, published studies and source materials have been grouped according to their topic and their most important findings.

Table 1.

Literature on energy consumption and environmental performance of dairy plants

Research topic	Authors
Energy consumption (direct) and energy conservation	Budny & Weiss [2000], Baker & McKenzie [2005], Benedetti et al. [2016], Biglia et al. [2015], Boutaghriout et al. [2016], Bühler et al. [2018], Cosgrove et al. [2016], De Lima et al. [2018], Domínguez-Niño et al. [2017], Energy Performance Indicator Report [2001], Fushimi & Fukui [2014], Gugała et al. [2015], IFC – World Bank Group [2007], Herbst & Griffiths [1993], Kalla et al. [2017], Kapela et al. [2015], Marchi et al. [2022], Masanet et al. [2014], Milani et al. [2011], Pradella et al. [2017], Rad & Lewis [2014], Ramirez et al. [2006], Rao [1986], Samarin et al. [2020], Srinivasan et al. [2018], Wardrop Engineering Inc. [1997], WS Atkins [1998], Wojdalski [1991], Xu & Flapper [2009], Xu et al. [2012], Zhang et al. [2018].
Heat recovery	Atkins et al. [2011], Atkins et al. [2010], Jbira et al. [2023]
Energy consumption in wastewater treatment	Kowalczyk & Karp [2005], Dąbrowski & Żyłka [2015], Grala et al. [2010], Kowalczyk & Bąbała [2010], Krzemińska et al. [2013].
Renewable energy	Schnitzer et al. [2007], Coskun et al. [2012]
Cleaner production standards	Bosworth et al. [2000], Honkasalo et al. [2005].
Best available techniques	Bosworth et al. [2000], WS Atkins [2005]
Environmental performance	Burnett & Hansen [2008], Honkasalo [2003], Honkasalo et al. [2005], Maxime et al. [2006], Prasad et al. [2004], Prasad & Pagan [2006], Zielińska-Chmielewska [2020].
Energy efficiency and optimization of selected production processes	Alves et al. [2014], Baker [2005], Bouman et al. [1993], Brush et al. [2011], Dobry et al. [2009], Đurić et al. [2005], Erbay et al. [2015], Gawałek & Wesołowski [2008], Jin & Chen [2009; 2011], Johnson & Langrish [2017], Jokandan et al. [2015], Kaleta & Chojnacka [2009], Kemp [2012], Leszczyńska & Lee [2016], Marks & Gut [2007], Moejes & Van Boxtel [2017], Munir et al. [2016], Murphy et al. [2013], Niamsuwan et al. [2013], Palianechka et al. [2022], Patel et al. [2009], Philipp et al. [2018], Písecký [2005; 2012], Raghavan et al. [2004], Seydel et al. [2006], Sikirica et al. [2003], Sorgüven & Özilgen [2012], Taghizadeh-Tabari et al. [2016], Walmsley et al. [2013], Yildirim & Genc [2017].
Financial and environmental implications of energy management	Aymerich et al. [2015], Chauhan et al. [2006], Dalton et al. [2002], Egas et al. [2021], Geary et al. [2010].
Process integration	Atkins et al. [2010; 2011], Chen et al. [2022], Kapustenko et al. [2008], Jbira et al. [2023], Kemp [2005], Lincoln et al. [2022], Schlossera et al. [2019], Walmsley et al. [2016; 2018].
Pulsed electric field processing	Bendicho et al. [2002], Guerrero-Beltrán et al. [2010], Sepulveda et al. [2005]
Solar energy	Boutaghriout et al. [2013], Camci [2020], Panchal et al. [2018]), Schnitzer et al. [2007]
Electric power substitution/electrification	Bühler et al. [2019]
Life cycle assessment (LCA), carbon footprint (CF), sustainable development standards	Djekič et al. [2014], Egas et al. [2021], Kim et al. [2013], Prakash & Henham [2014], Vasilaki et al. [2016], Vergé et al. [2007], Von Keyserlingk et al. [2013], Walmsley et al. [2015]
Cleaner production and the environmental impact of dairy production	Dvarionienė et al. [2012], Dyer et al. [2008], Komisja Europejska [2010], Nutter et al. [2013], Özbay & Demirer [2007], Patankar et al. [2010], Saunders & Barber [2007]
Cleaning of production equipment	Jeurnink & Brinkman [1994], Mierzejewska & Diakun [2011], Muthukumaran et al. [2004], Piepiórka-Stepuk et al. [2017]

Research on rational energy consumption in dairy plants (Table 1) has demonstrated that the impact of technical and technological factors on energy consumption and energy efficiency of dairy production in various seasons of the year has not been fully elucidated. The studies reviewed here examine the structure of energy demand in different production sectors, as well as the overall energy consumption in dairy plants. Milk is processed into various products, and energy consumption per unit of end product cannot be accurately determined. According to the Energy Performance Indicator Report, the energy consumption of Clean-In-Place (CIP) systems in Canadian dairies reaches 0.1 – 93.0 kWh/m³ of milk.

Water is also an energy carrier in cooling operations and in the production of steam for thermal processing. In most dairy plants around the world, water consumption ranges from 1 – 10 m³/1000 L of milk [Wojdalski et al. 2013]. Water consumption determines the amount of generated wastewater, which affects the plant’s overall energy demand. Energy consumption in wastewater treatment stations operated by dairy plants was analysed by Kowalczyk & Karp [2005] and Kowalczyk & Bąbała [2010]. The results of these studies are presented in Table 2.

Table 2.

Selected indicators and factors for analysing the energy performance of dairy plants

Indicators and factors for analysing energy performance	Unit of measure	Type of plant/production profile/product	Indicators			Source
Indicators and factors for analysing energy performance	Unit of measure	Type of plant/production profile/product	Range according to Fig. 2	Mean	Interval/season	Source
Electric energy consumption	kWh/m³ of raw milk	Total production	W_Z	90	10 – 680	WS Atkins [1998]
	kWh/m³ of milk	Milk powder, cheese, butter, yogurt, dairy desserts		27	-	Budny & Weiss [2000]
		Cheese, tvorog, yogurt, dairy desserts		32	-
		Cheese, butter, yogurt, dairy desserts		-	34 – 52
		Butter, milk powder, yogurt, dairy desserts		-	53 – 57
		Cheese, milk powder, butter		64
		Cheese, butter		65
		Yogurt, dairy desserts, tvorog, butter		66
	kWh/m³ of milk	Cheese	W_T	-	2.9 – 6.6
	kWh/Mg of butter	Butter (continuous method)	W_T	-	12.9 – 17.0
	kWh/Mg of milk	Milk processing output (based a study of three dairy plants)	W_Z	-	30 – 80	Boutaghriou et al. [2016]
	kWh/Mg of end product	Milk powder	W_Z	429.9	377.0 – 482.9	Budny et al. [1984a]
Heat consumption	GJ/m³ of raw milk	Total production	W_Z	2.01	0.12 – 14	WS Atkins [1998]
	MJ/m³ of processed milk	Milk powder, cheese, butter, yogurt, dairy desserts		1348	-	Budny & Weiss [2000]
		Cheese, tvorog, yogurt, dairy desserts		1439	-
		Cheese, butter, yogurt, dairy desserts		-	1583 – 2380
		Butter, milk powder, yogurt, dairy desserts		-	2733 – 3171
		Cheese, milk powder, butter		2882	-
		Cheese, butter		1884	-
		Yogurt, dairy desserts, tvorog, butter		2698	-
	GJ/Mg of evaporated water	Spray-drying	W_A	4.87	3 – 20	Baker & McKenzie [2005]
	GJ/Mg of processed milk	Modern dairy plants with heat recovery systems	W_Z	0.34	-	Bosworth et al. [2001]
		Modern plants where hot water is used as an energy carrier		0.50	-
		Older plants where steam is used as an energy carrier		2.00	-
Total energy consumption	MWh/Mg of milk powder	Milk powder (mechanical vapor recompression, MVR)	W_P	3.08	-	Bühler et al. [2019]
	GJ/Mg of product	Milk powder	W_Z	29.95	26.41–33.49	Budny et al. [1984a]
	MWh/Mg of dry product	Spray-drying of cheese whey	W_A	2.049	-	Domínguez-Niño et al. [2018]
Fuel gas	kWh/Mg of milk	Total production (based on a study of three production plants)	W_Z	-	100 – 220	Boutaghriou et al. [2016]
Total energy consumption	GJ/m³ of processed milk^*	Milk, tvorog (based on a study of a selected dairy plant)	-	3.04	1.93 – 2.47 Summer	Wojdalski et al. [2007]
	GJ/m³ of processed milk^*		-	3.04	3.63 – 4.19 Winter
	GJ/m³ of processed milk^**		W_Z	2.46	1.40 –1.96 Summer
	GJ/m³ of processed milk^**		W_Z	2.46	3.04 – 3.55 Winter
Total energy consumption	kWh/kg of evaporated water	Three-phase evaporator	W_A	0.14	-	Prasad et al. [2004]
Total energy consumption	kWh/kg of evaporated water	Five-phase evaporator	W_A	0.085	-	Prasad et al. [2004]
Total energy consumption expressed in fuel equivalents	kg of fuel equivalent/m³ of processed milk^*	Milk, tvorog (based on a study of a selected dairy plant)	W_Z	104	66 – 84 Summer	Wojdalski et al. [2007]
	kg of fuel equivalent/m³ of processed milk^*			104	124 –143 Winter
	kg of fuel equivalent/m³ of processed milk^**			84	48 – 67 Summer
	kg of fuel equivalent/m³ of processed milk^**			84	103–121 Winter
Energy consumption in wastewater treatment	kWh/kg BOD₅	Total production	W_T	-	0.89 – 3.22	Kowalczyk & Karp [2005]
Energy consumption in wastewater treatment	kWh/m³ of wastewater	Total production	W_T	-	1.94 – 6.32	Kowalczyk & Karp [2005]
SO₂ emissions	kg/m³ of raw milk	Total production	W_Z	1.65	0.05 – 22.4	WS Atkins [1998]
SO₂ emissions	kg/m³ of processed milk	Total production	-	0.83	-	Wojdalski et al. [2007]
NO_x emissions	kg/m³ of raw milk	Total production	W_Z	0.49	0.03 – 6.4	WS Atkins [1998]
NO_x emissions	kg/m³ of processed milk	Total production	-	0.60	-	Wojdalski et al. [2007]
CO₂ emissions	kg/m³ of raw milk	Total production	W_Z	1.97	0.01 – 9.23	WS Atkins [1998]
CO₂ emissions	kg/m³ of processed milk	Total production		1.25	-	Wojdalski et al. [2007]
Carbon footprint of end products	kg CO_2eq/kg of butter	Butter	W_Z	8.1	-	Flysjö et al. [2014]
	kg CO_2eq/kg of milk powder	Milk powder		7.4	-
	kg CO_2eq/kg of cheese	Cheese		6.5	-
	kg CO_2eq/kg of dairy beverage	Dairy beverages		1.2	-
Particulate-matter emissions	kg/m³ of raw milk	Total production		1.3	0.01 –14.4	WS Atkins [1998]
Particulate-matter emissions	kg/m³ of processed milk	Total production	W_Z	1.15	-	Wojdalski et al. [2007]
Black carbon emissions	kg/m³ of raw milk	Total production	W_Z	19	0.01 – 193	WS Atkins [1998]
Black carbon emissions	kg/m³ of processed milk	Total production	-	0.04	-	Wojdalski et al. [2007]

*

- heat and electric energy consumption, converted to primary energy, where 1 kWh = 3.6 MJ/0.3 (an energy conversion efficiency ratio of 0.3 indicates that energy is effectively converted and transported);

**

- heat and electric energy consumption, converted to primary energy, where 1 kWh = 3.6 MJ

3.2.

Evaluation of the Use of Energy Carriers

The data presented in Table 1 indicate that the energy consumption and environmental impact of dairy plants are complex phenomena. Various indicators have been applied to assess energy management and the conversion efficiency of different energy carriers. A diagram of a dairy plant as a user of energy carriers is presented in Figure 1 to organise the results of the study.

Energy efficiency in dairy plants, including energy consumption, is usually evaluated based on indicators of energy consumption per unit of end product (W_P, W_Z) or using energy efficiency indicators (EE). Energy efficiency is determined by dividing a plant’s output by the amount of energy input in the production process (energy consumption).

Energy consumption in the production process can thus be analysed based on a model of a dairy plant as a user of energy carriers (Fig. 1). Energy consumption per unit of end product (W_Z) is calculated by analysing all production processes where energy is used and converted to other forms of energy. Indicators of aggregate energy consumption (W_A), energy consumption per technological process (W_T), and energy consumption per production process (W_P) (Fig. 2) are less informative in analyses of energy consumption and energy efficiency (EE) in a dairy plant.

In reference to Figure 1, a band diagram of energy transformation processes at different reference levels, with energy changes (Q_n) at different stages/levels of energy transformation (Q₂, Q₃) and energy losses (Q_1,), is presented in Figure 2.

As shown in Figures 1 and 2, energy consumption in dairy plants can be divided into the following stages:

The consumption of energy (A_e, A_c) supplied by devices 5 – 7 (Fig. 1) to nodes 1 – 4 is measured at level A. The results are used to calculate energy consumption per unit of end product (W_e, W_c) and energy efficiency (indicators EE, EE_el, and EE_therm, which are the inverse of indicators W_e and W_c). The demand for energy is estimated at level A [Trojanowska 2010] to optimise electric energy consumption in the plant [Aymerich et al. 2015; Bergamini et al. 2021; Olkowski et al. 2023; Rad & Lewis 2014].
Energy conversion and further transformation of parameters from level A (items 8 –10 in Fig. 1). The production indicators of energy consumption per unit of end product (W_P) and the energy efficiency of production (EE_P) are presented in Figure 2. Energy and heat can be co-generated at this stage. For example, biogas supplied by a biogas plant (item 15 in Fig. 1) can be used as a source of energy [Bassols et al. 2002; Cannistraro et al. 2016; Leal & Silveira 2002].
Energy consumption. This level involves the machines and devices used in the analysed plant (item 11 in Fig. 1), as well as energy inputs into production processes that have not been converted to other energy carriers – e.g., fuel consumption in the milk evaporator; diesel consumption in transport; hot water in the heat exchanger; steam heat; heat generated by machines; cooling; lighting; and generation of negative pressure. This stage can be significantly expanded in plants where waste energy is recovered (e.g., streams Q₄ and Q₅ in Fig. 2). Energy recovery can thus be taken into consideration in the Sankey diagram. This level also involves energy consumption per technological process (W_T). Indicators of the aggregate energy consumption of the machines and devices that make up specific processing lines (W_A) and energy efficiency indicators (EE_T and EE_A) are less informative. In Figure 1, item 12 denotes the overall stream of waste products and pollutants emitted into the air. Item 14 denotes treated wastewater. Sewage sludge (item 16) can be used in fertilizer production [Chmielina & Kondrat 2024].
Total energy consumption, namely the overall energy input (A₄) required to manufacture the end product (Z; item 13 in Fig. 1) in a dairy plant. Level D is the theoretical reference level for input energy. Calculations of final energy consumption were presented by Rusen et al. [2018] and Walker et al. [2017].

Table 2 presents a synthesis of selected research findings and data from the cited sources regarding energy consumption and energy efficiency in various types of dairy plants with different production profiles, including pollutant emissions. These numerical values can be used by dairy plants to compare with or verify data obtained in practice. Andersson et al. [2018] and Elmaraghy et al. [2017] benchmarked the energy performance of production plants relative to either the top performers in a sector or best-practice techniques.

Energy consumption in different departments of the analysed dairy plant is presented in Table 3. Steam generation was the most energy-intensive process, accounting for 48.3 – 49.6% of the total energy consumption of the plant (including operations and the structure of energy carriers). Heat accounted for 57.2% of the total energy consumed by the plant when direct fuel consumption was included in the analysis.

Table 3.

Structure of energy consumption in the analysed dairy plant

Process/operation	[kJ/Mg of milk]					Percent share [%]
Process/operation	Steam	Direct fuel consumption	Refrigeration	Other machines and devices	Total	Percent share [%]
End product storage	-	-	41,868	-	41,868	9.46
Packing	-	3,489	-	-	3,489	0.71
Storage before packing	-	-	20,934	-	20,934	4.73
Deodorissation	5,815	-	-	-	5,815	1.32
Cooling	-	-	19,771	-	19,771	4.47
Homogenisation	-	-	-	2,326	2,326	0.53
Pasteurisation	213,992	-	-	-	213,992	48.30
Separation	-	-	-	41,868	41,868	9.46
Clarification/Standardisation	-	-	-	20,934	20,934	4.73
Reception and Storage	-	30,238	41,868	-	72,106	16.29
Total	219,807	33,727	124,441	65,128	443,103	100

Source: Brush et al. [2011] (based on Sikirica et al. [2003])

It should be noted that in the literature [Bosworth et al. 2000; WS Atkins Poland 2005] energy consumption per unit of end product was calculated based on measurements conducted across a small number of dairy plants. In other sources [IFC-WBG 2007], the detailed characteristics of dairy plants were not considered in the process of calculating energy consumption per unit of end product. In several studies, partial attempts were made to explain the correlations between selected factors related to production technology and technical equipment vs. energy consumption in dairy plants [Budny et al. 1982, 1984 a, b; Baker & McKenzie 2005; EPIR 2001]. The literature review conducted as part of the present study provided valuable information about the correlations between engineering and production factors, production profile, technical factors, and energy consumption in dairy plants.

4.

MATERIALS AND METHODS

The calculations and the study were conducted based on input data, the results of measurements, and the materials obtained from 140 dairy plants analysed in summer and 79 plants analysed in winter. Dairy plants for the study were selected randomly with regard to their daily milk processing outputs, production profiles, and technical equipment, with a special emphasis on the installed capacity of the electrical equipment. As in the study by WS Atkins [1998], energy consumption was determined based on the results of energy audits and questionnaires completed by dairy plants. Polish dairy plants were divided into four types (T₁–T₄) based on similarities in their production profiles (Table 4).

Table 4.

Types of examined dairy plants

Type	Production profile
T1	Milk, dairy beverages, cream, tvorog, cheese, butter, casein, milk powder
T2	Identical to T1 plants, excluding milk powder
T3	Identical to T1 plants, excluding milk, dairy beverages, cream, and tvorog
T4	Milk, dairy beverages, cream, and tvorog only

Each type of dairy plant (Table 4) was characterized using the three groups of factors (FR1-FR3) that are presented in Table 5. The energy consumption indicators W_c and W_e – defined by Budny et al. [1984 a, b] and Wojdalski & Dróżdż [2012] – were used in the study because of their applicability to industrial practice.

Table 5.

Factors that influence energy consumption in dairy production

Group of factors	Measured parameters	Symbols^*
FR1	Basic operating parameters and process automation	K_m, K_p1, K_p2, N₁, N₂, P, U_en1, U_en2, Z, Z_m, Z_4d
FR2	Production profile^**	Z₁, Z₂, Z₃, Z₄, Z₅, Z₆, Z₇, Z₈, Z₉, Z₁₀, Z₁₁, Z₁₂, Z₁₃, Z₁₄, Z₁₅, Z₁₆, Z₁₇, Z₁₈, Z₁₉, Z₂₀, Z₂₁, Z₂₂, Z₂₃
FR3	Technical equipment	P₁, P₂, P₃, P₄, P₅, P₆, P₇, P₈

*

- the analysed parameters are explained in the Symbols and Abbreviations subsection at the beginning of the paper

**

- the production profile includes the organisation of production processes

Group FR1 factors for each type of analysed dairy plant (T1, T2, T3, and T4) are presented in Table 6.

Table 6.

General characteristics of the analysed types of dairy plants in summer and winter

FR1	Type of plant (Summer)				Type of plant (Winter)
FR1	T1	T2	T3	T4	T1	T2	T3	T4
K_p1	0.59 – 3.10	0.36 – 5.66	0.54 – 3.10	0.62 – 2.42	0.32 – 2.90	0.22 – 2.99	0.25 – 2.90	0.40 – 1.16
K_p2	0.35 – 1.33	0.23 – 3.54	0.30 – 2.13	0.14 – 0.82	0.19 – 1.18	0.13 – 2.46	0.13 – 1.35	0.18 – 0.90
U_en1	3.52 – 17.74	3.93 – 40.46	5.30 – 31.67	1.13 – 21.40	4.13 – 17.74	1.16 – 39.55	1.17 – 17.74	2.26 – 13.53
U_en2	2.43 – 12.91	1.26 – 23.75	3.06 – 23.75	0.49 – 8.93	0.70 – 13.16	1.02 – 32.41	1.08 – 11.92	1.31 – 8.77
K_m	3.74 – 13.69	2.63 – 28.79	4.02 – 28.79	0.99 – 24.66	1.76 – 24.66	1.99 – 34.33	1.99 – 32.26	3.38 – 24.64

For example, in type T1 dairy plants (Table 6) producing milk powder, the value ranges of the analysed indicators were generally determined to be K_m = 3.8 – 13.7 kW/1000 L; U_en1 = 5.3 – 13.5 kW/person; and K_p1 = 600 – 3100 L/person. In type T3 dairy plants, these ranges of these indicators were generally determined to be K_m = 4.1 – 28.0 kW/1000 L; U_en1 = 6.0 – 17.0 kW/person; and K_p1 = 600 – 2800 L/person. It was assumed that technological and technical factors in dairy plants (FR2, FR3) (Table 5) were correlated with energy consumption (A_e, A_c, W_e, and W_c). Similar groups of factors were used by other researchers (e.g., Budny et al. [1984b]) to evaluate their correlations with energy consumption in the dairy sector. Group FR1 consists of 11 factors describing overall production profile, along with selected engineering and production factors. Previous studies demonstrated that total installed capacity (P) and daily milk processing output (Z) are useful parameters for evaluating energy consumption in dairy plants.

In the present study, these factors were analysed jointly as the FR1 group of factors. Previous research methods were adapted for this study. A review of the literature revealed that these factors play an important role in industrial practice, which is why FR1 factors were included in mathematical calculations to describe the complexity of the studied problem.

Cause-and-effect relationships in energy consumption were also analysed. The independent variables examined express the research concept adopted at the level of plant design. The automation of production processes, relative to employment in production departments, total employment, and the organization of production processes, were expressed by indicators U_en1, U_en2, and K_m, respectively. Daily milk processing output per employee was expressed by indicators K_p1 and K_p2. These indicators can be used to evaluate a plant’s technical equipment and the organization of production processes.

Group FR2 consists of 11 technological factors denoting the analysed plants’ production profiles and total output. Selected FR2 group factors were identified in the analysed plants (Table 2). Group FR3 factors represent the installed capacity of production machinery and processing lines, and are related to the technical equipment in the analysed plants. The respective correlations (r) between group FR2 and FR3 factors and dependent variables A_e, A_w, W_e, and W_c, were determined in the statistical analysis. The value of the correlation coefficient (r) was also used to determine the strength of the relationships (coefficient of determination, R²) between the analysed factors and energy consumption (A_e and A_c), as well as the energy consumption per unit of end product (W_e and W_c).

The results obtained for each group of dairy plants, as well as for individual plants, were processed statistically using the stepwise regression method. Stepwise multiple regression was chosen due to the large number of variables with different levels of significance. Energy consumption (A_e, A_c, W_e, or W_c) in the studied types of plants was determined using the following general formula: (1) $Y = b_{0} + b_{1} x_{1} + b_{2} x_{2} + \dots \dots .. + b_{k} x_{k}$ Y = {b_0} + {b_1}{x_1} + {b_2}{x_2} + \ldots \ldots .. + {b_{\rm{k}}}{x_{\rm{k}}} where: Y = dependent variables A_e, A_c, W_e, or W_c; x = group FR2 and FR3 factors (e.g., P_n and Z_n) (Table 5) in analyses of different types of dairy plants, and group FR1 factors (Z_m and Z_4d) and group FR2 factors (Z_n) in analyses of individual plants; Regression coefficients (b_i) were calculated for the resulting equations.

Equations that met all of the below criteria were selected:

–
Correlation (r) and determination (R²) coefficients reached high values (r > 0.60 and R² > 0.40) in the analysed types of dairy plants (T1 – T4);
–
The number of independent variables did not exceed 4, and the equation did not have a complex form and involved group FR2 and FR3 factors (Table 5);
–
The equation contained factors that were not bound by a cause-and-effect relationship (i.e., coincidence);
–
F_obl > F_tabl in the F-distribution test at α = 0.05;
–
t_obl > t_tabl in Student’s t-test (α = 0.05) for the group consisting of FR1, FR2, and FR3 factors.

The formulas that met the above criteria: (2) $b_{1} x_{1} + b_{2} x_{2} + \dots \dots .. + b_{k} x_{k} \geq b_{0}$ {b_1}{x_1} + {b_2}{x_2} + \ldots \ldots .. + {b_k}{x_k} \ge {b_0} where x_i ≥ 0 for i = 1......k were used to determine processes and devices that influence energy consumption in the types of dairy plants examined (T1, T2, T3, T4). Regression coefficients b_i was also used in the statistical analysis. Conditions in which all factors x_i = 0 in a single equation were ruled out. The collected data were processed statistically to obtain linear regression equations (Tables 4 – 8) expressing variations in energy consumption. Statistical analyses were conducted using the Polish version of Statistica 12. Selected formulas described by Wojdalski [1991] were also applied. The methodologies presented by Rodriguez-Gonzalez et al. [2015], Schmidt et al. [2016], Schulze et al. [2017], Tomasula et al. [2013], Zisopoulos et al. [2017], Menghi et al. [2019], and Munir et al. [2014] can be also used to evaluate energy consumption in production plants.

5.

RESULTS

5.1.

Types of dairy plants

Tables 7 – 10 present the results of the analysis examining the impact of the installed capacity of electrical devices and the production profile of different types of dairy plants (Tables 4 and 6) on total energy consumption as well as energy consumption per unit of end product.

Table 7.

Influence of the daily production profile and the installed capacity of electrical devices on energy consumption in T1 dairy plants (producing milk powder)

Season	Total installed capacity P [kW]	Average energy consumption		Average energy consumption per unit of end product		Multiple regression equation	Coefficient of determination R²	Range of variation in independent variables
Season	Total installed capacity P [kW]	A_e [kWh/24h]	A_c [GJ/24h]	W_e [kWh/1000 L]	W_c [GJ/1000 L]	Multiple regression equation	Coefficient of determination R²	Range of variation in independent variables
S	454.0 – 2,421.7 (21)	7,682.1	432.650	41.73	2.3207	A_e = 3944.70 + 37.110 Z₄	0.568	Z₄:19.0 – 335.8
						A_c = 134.293 + 0.546P₂ + 0.760P₄	0.789	P₂: 31.0 – 777.0 P₄: 100.0 – 964.0
						A_c = 140.56 + 2.433Z₄ + 1.977Z₅ + 0.815 Z₈	0.834	Z₄: 19.0 – 335.8 Z₅: 0 – 81.8 Z₈: 0 – 335.8
W	318.0–2,548.0 (19)	6,246.0	396.632	52.70	3.4125	A_e = 4289.00 + 41.401Z₄ for W_c R² < 0.20	0.502	Z₄: 10.4 –282.7
						A_c = 83.369 + 0.530P₂ + 0.644P₄ + 0.857P₁₀ for W_c R² < 0.34	0.846	P₂: 31.0 – 777.0 P₄: 100.0 – 964.0 P₁₀: 0.0 – 358.3
						A_c = 246.86 + 3.168 Z₄	0.546	Z₄: 10.4 – 282.7

Table 8.

Influence of the daily production profile and the installed capacity of electrical devices on energy consumption in T2 dairy plants (not producing milk powder)

Season	Total installed capacity P [kW]	Average energy consumption		Average energy consumption per unit of end product		Multiple regression equation	Coefficient of determination R²	Range of variation in independent variables
Season	Total installed capacity P [kW]	A_e [kWh/24h]	A_c [GJ/24h]	W_e [kWh/1000 L]	W_c [GJ/1000 L]	Multiple regression equation	Coefficient of determination R²	Range of variation in independent variables
S	83.0 – 2,935.0 (37)	3,289.9	118.909	39.52	1.3607	A_e= 748.34 + 60.1453Z₁ + 39.4929Z₃ + 22.079Z₅ + 75.286Z₈ for W_e R² < 0.32	0.442	Z₁: 0 – 211 Z₃: 0 – 164 Z₅: 0 – 141 Z₈: 0 – 157.5
						A_c= 1.152 + 0.310P₁ + 1.266P₂ + 1.082P₆ for W_c R² < 0.37	0.860	P₁: 24.4 – 1155.1 P₂: 0.0 – 434.0 P₆: 250.0 – 956.0
						A_c= 18.295 + 1.501Z₁ + 1.748Z₂ + 0.990Z₃ +1.006 Z₅ −5.385 Z₇ + 5.265 Z₈	0.830	Z₁: 0 – 211 Z₂: 0 – 73 Z₃: 0 – 164 Z₅: 0 – 141 Z₇: 0 – 44.4 Z₈: 0 – 157.5
W	30.3 – 1,938.0 (24)	2129.3	120.581	39.92	2.4672	A_e = 864.92 + 94.4138Z₂ + 104.032Z₈ for W_e R² < 0.20	0.603	Z₂: 0 – 63.6 Z₈: 0 – 75.6
						A_c = −4.109+0.364P₁ +0.989P₂+ 1.080P₇ + 0.191P₁₀ for W_c R² < 0.33	0.896	P₁: 30.3 – 708.0 P₂: 0.0– 312.0 P₇: 0.0 – 68.0 P₁₀: 0.0 – 377.0
						A_c = 26.259+2.981Z₁ +2.492Z₃ + 15.157Z₇ + 6.733Z₈	0.693	Z₁: 0 – 111.6 Z₃: 0 – 64.2 Z₇: 0 – 9.9 Z₈: 0 – 75.6

Table 9.

Influence of the daily production profile and the installed capacity of electrical devices on energy consumption in T3 dairy plants (not producing milk, dairy beverages or tvorog)

Season	Total installed capacity P [kW]	Average energy consumption		Average energy consumption per unit of end product		Multiple regression equation	Coefficient of determination R²	Range of variation in independent variables
Season	Total installed capacity P [kW]	A_e [kWh/24h]	A_c [GJ/24h]	W_e [kWh/1000 L]	W_c [GJ/1000 L]	Multiple regression equation	Coefficient of determination R²	Range of variation in independent variables
S	83.0 – 1,923.0 (22)	3,079.5	148.151	37.83	1.5542	A_e = 953.19 + 29.707Z₄ + 430.898Z₆	0.932	Z₄: 10.0 – 335.8 Z₆: 0 – 22.5
						W_e = 42.633 - 0.481Z₅	0.346	Z₅: 0 – 80.3
						A_c = −36.781 + 0.607P₁ - 0.523P₃ + 1.037P₄ + 0.968P₆ + 5.555P₁₀	0.990	P₁: 30.3 – 574.0 P₃: 0.0 – 320.0 P₄: 0.0 – 592.0 P₆: 0.0 – 58.6 P₁₀: 0.0 – 101.0
						W_c = 1.476 + 0.001P₄ - 0.012P₆+0.013P₁₀	0.662
						A_c = 41.899 - 0.964Z₃ + 2.204Z₄ + 15.219Z₆	0.971	Z₃: 0 – 164.2 Z₄: 10.0 – 335.8 Z₅: 0– 80.3 Z₆: 0 – 22.5
						W_c = 1.6316-0.006Z₃ + 0.003Z₄ - 0.013Z₅	0.608	Z₃: 0 – 164.2 Z₄: 10.0 – 335.8 Z₅: 0– 80.3 Z₆: 0 – 22.5
W	30.3 – 1,938.0 (20)	3,548.8	230.243	42.82	2.9086	A_e = 2133.00 - 85.291Z₄ W_e = 32.375 + 5.757Z₆	0.660 0.770	Z₄: 0 – 53.0 Z₆: 0 – 7.9
						A_c = 1.145 + 0.444P₁ + 0.936P₄ + 1.275P₇	0.985	P₁: 30.3 – 470.0 P₄: 0.0 – 592.0 P₇: 0.0 – 87.0
						W_c = 2.2652 + 0.028P₇	0.627	P₁: 30.3 – 470.0 P₄: 0.0 – 592.0 P₇: 0.0 – 87.0
						A_c = 117.423 + 62.187Z₆	0.399	Z₆: 0 – 7.9
						W_c = 2,3795 + 0.291Z₆	0.563	Z₆: 0 – 7.9

Table 10.

Influence of the daily production profile and the installed capacity of electrical devices on energy consumption in T4 dairy plants (producing only milk, dairy beverages, tvorog, and, in some cases, also butter)

Season	Total installed capacity P [kW]	Average energy consumption		Average energy consumption per unit of end product		Multiple regression equation	Coefficient of determination R²	Range of variation in independent variables
Season	Total installed capacity P [kW]	A_e [kWh /24h]	A_c [GJ/24h]	W_e [kWh/1000 L]	W_c [GJ/1000 L]	Multiple regression equation	Coefficient of determination R²	Range of variation in independent variables
S	50.0 – 2,385.0 (34)	2,374.1	59.305	32.13	1.0882	A_e = 559.42 + 29.374Z₁	0.834	Z₁: 2.5 – 80.6
						A_e =345.63 + 4.420P₁ + 18.787P₂	0.950	P₁: 0.0 –1785.0 P₂: 7.5 –170.0
						A_c = 18.658 + 0.460 Z₁ + 1.766Z₂	0.849	Z₁: 2.5 – 80.6 Z₂: 3.1 – 39.6
W	63.0 – 646.0 (24)	1,081.4	52.366	30.70	1.7502	A_e = 358.08 + 35.352Z₁	0.672	Z₁: 1.0 – 59.1
						A_e = 463.75 + 6.235P₃	0.429	P₃: 6.0 – 250.0
						A_c = −0.121 + 0.531P₁ − 1.899P₇	0.811	P₁: 17.4 – 273.7 P₇: 0.0 – 41.2
						A_c = 30.777 + 1.054Z₁	0.441	Z₁: 1.0 – 59.1

The obtained regression equations present change trends in energy consumption in the analysed types of dairy plants. The regression coefficient denotes the influence of a given independent variable on the dependent variable in each type of dairy plants on the assumption that the remaining independent variables in the equation are constant. In analyses of dairy production, changes in energy consumption can be reliably predicted with the use of regression equations.

The data presented in Tables 7 – 10 were visualized in Figures 3 – 6 to compare the results of the calculations.

In summer, the production profile of the studied plants exerted a greater influence on heat consumption than on electric energy consumption. For example, in T3 plants, R²∙100% was determined to be 97.1% and 93.2%, respectively. In winter, the above trend was seen only in T1 and T2 plants.

Installed capacity exerted a significant effect, mostly on heat consumption (A_c). The greatest influence was noted in T3 plants, where R²∙100% reached 98.5 – 99.0% in both summer and winter. At the same time, production profile and installed capacity only exerted a significant influence on heat consumption in T3 plants.

Season significantly affected energy and heat consumption in the types of dairy plants that were compared. Heat consumption per unit of end product (W_c) was higher by 47.0 – 87.14% in winter than in the summer. The greatest increase in heat consumption was noted in T3 plants. Electric energy consumption per unit of end product (W_e) increased by 13.3 – 27.2%, but only in T1 and T3 plants producing milk powder.

T1 plants were characterised by having the highest overall energy consumption (Figs. 7a and 8a). Milk powder production was the main technological factor making the greatest contribution to this, as shown by the values calculated for the T1(1) plant (Table 11)’s energy consumption in the production of milk powder (Table 2).

Table 11.

Technological factors influencing energy demand in selected dairy plants

Dairy plant specification	Average electric energy consumption	Season	Regression equations	Correlation coefficient r	Independent variables’ range of variation
*T1 (1)* P = 2500 kW K_m= 17.67 kW/1000L Z = 4,657,840 L/month	A_e = 488731.7 kWh/month	S/W	A_e = 38530 + 21.93Z_m	0.917	Z_m: 4,058.9 – 6348.8 m³/month
	W_e = 104.9 kWh/1000L	S/W	W_e = 178.6 - 0.019Z_m	−0.982	Z_m: 4,058.9 – 6348.8 m³/month
*T4 (1)* P = 5400 kW K_m = 6.9 kW/1000 L Z = 777,626 L/24h	A_e = 1842461 kWh/month	S/W	A_e = 80.184 - 74908Z_m	0.63	Z_m: 21749 – 25541 Mg/month Z₁₁: 3597 – 5653 Mg/month Z₁₂: 4,956 – 6240 Mg/month Z₁₃: 113 – 204 Mg/month Z₁₄: 456 – 782 Mg/month Z₁₅: 2184 – 3023 Mg/month
	A_e = 1842461 kWh/month		A_e = 155.75·Z₁₁+84.68·Z₁₂ - 573.45·Z₁₃–11.36·Z₁₄+232.33·Z₁₅ + 139383.53	0.72
	W_e = 77.0 kWh/1000 L		W_e = 0.0002 Z_m + 72.558	Weak correlation
	W_e = 77.0 kWh/1000 L		W_e = 3∙10⁻³ Z₁₁ + 7∙10⁻³·Z₁₂ - 0.017·Z₁₃ - 0.025·Z₁₄ + 3∙10⁻³·Z₁₆ + 36.5	0.31
	A_c = 17060.39 GJ/month		A_c = 0.0946Z_m + 14799	Weak correlation
	A_c = 17060.39 GJ/month		A_c= −1.49·Z₁₁ - 1.33·Z₁₂ + 3.46·Z₁₃ + 5.84·Z₁₄ + 3.93·Z₁₅ + 16572.40	0.65
	W_c = 0.71 GJ/1000 L		W_c= −3E-05Z_m + 1.3441	0.47
	W_c = 0.71 GJ/1000 L		W_c = −1∙10⁵·Z₁₁ - 3∙10⁵·Z₁₂ + 2∙10⁴·Z₁₃ + 7∙10⁻⁶ ·Z₁₄ + 1∙10⁻⁵ ·Z₁₅ + 0.99	0.83
*T4 (2)* P = 420 kW K_m = 3.2 kW/1000L Z = 132,765 L/24h	A_e = 101,516 kWh/month	S/W	A_e = 16.039 Z_m + 37632	0.741	Z_m: 2,678.12 – 5,276.36 m³/month Z₁₆: 5,171 – 9,293 dm³/month Z₁₇: 29,191 – 44,207 dm³/month Z₁₈: 24,000 – 299,045 dm³/month Z₁₉: 1,184–3,388 dm³/month Z₂₀: 3,962 – 35,914 kg/month Z₂₁: 10,593 – 13,790 kg/month Z₂₂: 37,806 –187,223 kg/month Z₂₃: 281 – 3079 kg/month
	A_e = 101,516 kWh/month		A_e = 0.17·Z₁₆ + 3.38·Z₁₇ + 0.32·Z₁₈ - 0.70·Z₁₉ + 1.62·Z₂₀ + 0.48·Z₂₁ + 0.08·Z₂₂ - 0.84·Z₂₃ - 15048.70	0.984
	W_e = 25.94 kWh/1000 L		W_e = −0.0025 Z_m + 35.795	0.526
	W_e = 25.94 kWh/1000 L		W_e = 5∙10⁻⁶ ·Z₁₆ - 0.002·Z₁₇ + 4∙10⁻⁶·Z₁₈ - 0.001·Z₁₉ - 0.004·Z₂₀ + 4∙10⁻⁶ ·Z₂₁ + 2∙10⁻⁶·Z₂₂ + 0.002·Z₂₃ + 59.29	0.905
*T4 (3)* P = 300 kW K_m = 6.6 kW/1000L Z_4d = 181,300 L/96 h	A_e = 10,681 kWh/4d	S/W	A_e = 84.71Z_4d - 4676.82	0.675	Z_4d: 173,430 – 192,500 L/4d
	W_e = 58.87 kWh/m³		W_e = 0.1479Z_4d + 31.75	0.285
	A_c = 56.93 GJ/4d		A_c = 0.6966Z_4d - 69.36	0.719
	W_c = 313.56 MJ/m³		W_c = 2.00 Z_4d - 48.81	0.482

According to Atkins et al. [2010], heat recovery from spray-drying in the production of milk powder can decrease hot air consumption by up to 21%. Domínguez-Niño et al. [2018] studied the influence of spray-drying on energy consumption, production costs, and the physicochemical characteristics of cheese whey. The analysed factors were inlet air temperature (180 – 220°C), outlet air temperature (80 – 100°C), and silica and maltodextrin additives (2% and 5%, respectively, w/w). Analysis of variance revealed that inlet and outlet air temperature and the applied additives exerted a significant effect (p < 0.10) on the response variables. The optimal drying conditions were an inlet air temperature of 180°C, outlet air temperature of 80° C, and additive content of 5%. Under these conditions, the output of spray-dried whey reached 0.2165 kg/h. The resulting product had a moisture content of 2.08% and water activity of 0.125. Production cost reached USD $17.06/kg, and energy consumption was determined to be 2.0490 kW · h/kg of dry product.

Bergamini et al. [2021] identified key measurement points for monitoring energy consumption in the production of milk powder. The greatest proportion of variance in energy consumption per unit of end product was explained in T3 plants. In the other types of dairy plants, the correlation coefficients (r), denoting the relationship between independent variables and indicators W_e and W_c, ranged from 0.40 to 0.60. The high energy consumption of T1 plants can be attributed to the fact that these plants produced up to nine different products. In turn, energy consumption was lowest in T4 plants, whose production profiles consisted of only 3 – 4 products. In all analysed cases, an increase in the number of different products manufactured increased energy consumption and energy losses (streams Q₃ and Q′₃ in Fig. 2).

Weaker correlations between the variables presented in Tables 7 – 10 indicate that energy was consumed outside the production process. The energy efficiency (EE) indicators of the studied plants (milk processing output per 1 GJ of consumed energy) are presented in Figures 8a and 8b.

Heat consumption accounted for 85 – 90% of total energy consumption, with the demand for thermal energy varying between seasons. The winter increase in heat consumption in the analysed types of dairy plants relative to the summer season is presented in Figure 9.

5.2.

Individual dairy plants

Measurements were conducted in selected individual T1 and T4 plants that were characterised by significant differences in energy consumption, with the goal of providing a more detailed view of the results obtained in these groups. The results of the analyses conducted in four dairy plants selected from the examined groups of plants are presented in Table 11 to illustrate the strong effect of the production profile on energy consumption.

Energy production was higher in T1 than in T2 plants. The values of indicator Wc were nearly twice as high (Figure 7a), and the values of indicator W_e were 30% – 70% higher in T1 than in T2 plants (Figure 8a). Additional parameters of the selected plants – installed capacity and the values of indicator K_m – are provided in Table 11. Selected correlation coefficients (r < 0.6) are also presented in Table 11.

Monthly milk processing output (Z_m) and four-day milk processing output (Z_4d) (FR1, Table 5) are the independent variables in Table 11 relative to the values presented in Tables 7–10. In the T1(1) plant (producing milk powder), monthly milk processing output (Z_m) was responsible for 84.0% of its electric energy consumption (r = 0.917). Monthly milk processing output (Z_m) also explained 96.4% of the variance in electric energy consumption per unit of end product (W_e) (r = 0.982). An increase in milk-processing output led to a significant decrease in electric energy consumption per unit of end product (W_e).

Strong correlations between production profile and energy consumption were observed in T4 plants. In the T4(1) plant, the production profile explained 68.9% of the variance in heat consumption per unit of end product, while in the T4(2) plant, the production profile explained 96.8% of the variance in electric energy consumption (A_e) and 81.9% of the variance in electric energy consumption per unit of end product (W_e). The correlations between independent variables were weaker for four-day milk processing output (Z_4d), as demonstrated by the results of the calculations for the T4(3) plant.

6.

SUMMARY AND CONCLUSIONS

The literature on the factors influencing energy consumption and the environmental impact of dairy plants was reviewed in this study. Various indicators were used to assess the management of energy carriers in the analysed plants. A diagram of a dairy plant as a user of energy carriers was presented to organise the results of the study. A detailed analysis of energy consumption was conducted in four different types of dairy plants with different production profiles. Information for modelling energy consumption in dairy plants and generating databases was provided. The results of the performed calculations and statistical analyses provide new insights about the correlations among the four types of dairy plants between engineering and production factors, production profile (milk processing and manufacture of end-products), technical equipment, and energy consumption. The results were used to formulate recommendations for reducing energy consumption, increasing energy efficiency, and implementing cleaner production standards in industrial practice.

The following conclusions can be formulated based on the results of the present study:

–
Heat consumption is highest in T1 dairy plants, which specialize in milk powder but may produce up to 8 other dairy products;
–
Installed capacity was a more effective predictor of heat consumption than technological factors describing a plant’s production profile, particularly in the summer (0.88 > r > 0.99). This observation can be used by designers to promote energy conservation measures during plant operation;
–
Production profile was the most effective predictor of electric energy consumption in dairy plants producing 2 – 4 products, in particular in T4 plants;
–
Energy consumption per unit of end-product varied across seasons and was correlated with technical equipment and process automation, but only in smaller T3 plants;
–
Energy efficiency was highest in T4 plants when they were analysed individually, due to their smaller range of thermal processing operations;
–
In analyses of individual dairy plants, energy consumption per unit of end-product was strongly correlated with both milk processing output and production profile. This indicates that work conditions and the results of energy audits should be analysed separately in individual plants;
–
The empirical formulas developed in this study, in particular the regression coefficients, can be used to perform detailed analysis, generate new data and reference materials, and model energy consumption processes in dairy plants;
–
Due to the structure of their independent variables, the presented empirical formulas can also be used to optimise energy consumption in different types of equipment.

Determinants of Energy Consumption in the Dairy Industry: A Case Study in Poland

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